This article explains the concept of instruction‑following in large language models, compares early and modern LLMs, details the training techniques that enable it, highlights its importance, offers practical prompting tips, and discusses current challenges and future directions.
This article examines why carefully crafted prompts often deliver higher efficiency and lower cost than fine‑tuning large language models, outlining the strengths, pitfalls, and practical decision‑making guidelines for choosing the right approach.
The article explains how integrating the Cursor AI assistant into front‑end development reshapes workflow by separating planning from execution, using iterative context loops for analysis, design, and impact assessment, guiding minimal‑change code generation and testing, and ultimately shifting developer skill from memorizing APIs to asking precise questions.
This article explains how frontend developers can use large language models to detect and prevent marketing content violations in WeChat mini‑programs, covering pain‑point discovery, LLM‑driven compliance architecture, prompt optimization, model selection, testing methods, and seamless frontend integration with Feishu notifications.
By following a seven‑step workflow—scanning Go code, discussing refactoring plans, creating structured Cursor rules, optionally deep‑diving into complex logic, iteratively applying rewrites with comments and tests, running AI self‑review, updating documentation, and performing full verification—developers combine AI speed with human judgment to efficiently refactor projects and reduce technical debt.
Prompt engineering uses in‑context learning to turn large language models into precise, task‑aware assistants by providing well‑crafted prompts that guide the model’s probability distribution, reduce hallucinations, and unlock hidden knowledge without any parameter tuning.
This article guides developers without an AI background through the fundamentals of building large‑language‑model applications, covering prompt engineering, multi‑turn interaction, function calling, retrieval‑augmented generation, vector databases, code assistants, and the MCP protocol for AI agents.
This tutorial walks through the limitations of simple prompt usage, introduces LangChain as a framework for building full‑featured LLM applications, explains its core concepts and components, and provides step‑by‑step code examples for installing, configuring, and running a basic LangChain demo.
This article walks readers through the fundamentals of prompt engineering—covering role, context, instruction, examples, and output formatting—then demonstrates a step‑by‑step construction of a sophisticated SQL‑generation prompt, complete with concrete code snippets, best‑practice tips, and reference resources.
This article explains Anthropic’s open‑source Model Context Protocol (MCP), its client‑server architecture, resource and tool definitions, sampling workflow, and provides step‑by‑step Python examples for building a PoE2 hot‑fix fetcher and a simple chatbot that leverages MCP to connect large language models with external data sources and functions.
This tutorial introduces the fundamentals of LangChain, explains large language models, prompt engineering, word embeddings, and demonstrates how to use the Java implementation LangChain4j with Maven dependencies, model I/O, memory, retrieval, chains, and agents to build sophisticated LLM‑driven applications.
The article examines how AI coding assistants are reshaping software engineers' roles—from hands‑on creators to overseers—triggering an identity crisis, while exploring the loss of coding joy, emerging practices like prompt engineering, and strategies to adapt and retain core engineering craftsmanship.
The article surveys Retrieval‑Augmented Generation (RAG) as a solution to large language model limits—such as outdated knowledge, hallucinations, and security risks—by integrating vector‑database retrieval with LLM generation, and discusses related tools, multi‑agent frameworks, prompt engineering, fine‑tuning methods, and emerging optimization trends.
Understanding multi‑agent AI systems, this article explains how ReAct’s tightly coupled reasoning‑action loop, the Model Context Protocol, and the open‑source OpenManus implementation enable autonomous task planning, tool invocation, and memory management, contrasting traditional chatbots with delivery‑centered agents while highlighting current limitations and future optimization needs.
This guide breaks down the structure of effective text‑to‑image prompts, shows how to refine descriptions, optimize keywords, use weighted terms and negative prompts, and provides concrete examples for various visual styles and application scenarios.
The article examines how chain‑of‑thought (CoT) templates are evolving from short‑term heuristics to long‑range planning in large language models, highlighting recent advances such as OpenAI o1, DeepSeek R1, and Kimi 1.5, and explores template designs that boost reasoning performance, efficiency, and multimodal capabilities.
The article outlines how AI‑generated essays often exhibit overly structured sections, repetitive phrasing, and mechanical punctuation, and then provides practical tactics—such as de‑structuring transitions, inserting personal anecdotes, swapping hot words, using irregular punctuation, adding examples, and de‑professionalizing tone—to mask these signatures and lower plagiarism detector scores.
This guide demonstrates how to integrate the Cursor AI editor with Git to automate commit message generation, streamline branch management, perform intelligent code reviews, resolve merge conflicts, and generate commands, offering concrete prompts, shortcuts, and visual examples that enhance version‑control efficiency for development teams.
This guide explains the typical characteristics of AI‑generated text and provides practical, step‑by‑step techniques—such as de‑structuring, logic reshaping, keyword substitution, punctuation tweaking, adding concrete examples, and using informal analogies—to make AI‑written content appear more human‑like and reduce detection risk.
This guide demonstrates building a steerable LLM‑based translation workflow using LangGraph, covering the initial translation, model‑generated reflection suggestions, and final improvement steps with full Python code examples and a complete execution result.
The article introduces Easy DataSet, an open‑source tool that streamlines the creation of domain‑specific datasets by aggregating public data sources, chunking Markdown documents, generating and managing QA pairs with configurable LLM endpoints, and exporting them in common formats, while outlining its architecture and future roadmap.
The article recounts a live‑streaming team’s six‑month experiment using large‑language‑model AI to boost backend, frontend, testing, data‑science and data‑engineering productivity, detailing goals, LLM strengths and limits, and practical tactics such as task splitting, input refinement, human‑AI guidance, retrieval‑augmented generation and fine‑tuning, while emphasizing disciplined task design, prompt iteration, and future vertical integrations.
The author shares practical experiences and reflections on using AI tools like Cursor and Claude 3.7 to develop a web application, highlighting the growing importance of demand analysis, workflow integration, and the nuanced impact of AI on software engineering jobs.
This article analyzes the emergence of DeepSearch and DeepResearch as next‑generation AI search frameworks, detailing their iterative search‑read‑reason loop, design decisions, implementation challenges, code architecture, and practical insights for building robust LLM‑driven agents in 2025.
The article presents a structured methodology for crafting DeepSeek prompts, covering a four‑element template, scenario‑specific techniques, high‑frequency use cases such as data analysis, document generation, and automation, advanced knowledge‑base customization, tool integration, reliability checks, workflow optimization, and common pitfalls.
This article presents a comprehensive case study on how large language models are integrated into 58.com’s real‑estate recommendation platform, detailing challenges, data adaptation, prompt and parameter optimizations, embedding generation, conversational recommendation, and future directions for multimodal and generative recommendation systems.
This guide shows non‑AI developers how to create large‑model applications by mastering prompt engineering, multi‑turn interactions, Retrieval‑Augmented Generation, function calling, and AI‑Agent integration, with practical code examples, tool design patterns, and deployment tips.
The article outlines three practical approaches to get the most out of DeepSeek—using it as a conversational assistant, integrating its API to power AI tools such as the Chrome immersive‑translation plugin, and leveraging it for AI‑assisted programming—while comparing the V3 and R1 models and offering concrete configuration steps.
This article provides a comprehensive introduction to developing large language model (LLM) applications, covering prompt engineering, zero‑ and few‑shot techniques, function calling, retrieval‑augmented generation (RAG) with embedding and vector databases, code assistants, and the MCP protocol for building AI agents, all aimed at non‑AI specialists.
The article examines the hype around Manus, an AI agent whose invitation codes sell for up to $10,000, by dissecting its interface, testing long‑text generation, price‑comparison, and financial‑analysis tasks, revealing reliance on existing tools, hallucination errors, high token costs, and offering open‑source alternatives.
This article introduces the fundamentals of prompt engineering and presents fourteen practical techniques—ranging from role‑playing and step‑by‑step reasoning to chain‑of‑thought and ReAct—that help users craft precise, high‑quality prompts for any large language model, dramatically improving AI output.
This article explains the technical principles behind Function Call and ReAct in large language models, provides code samples, compares their strengths and limitations, and illustrates each approach with practical scenarios such as smart customer service and financial analysis assistants.
This comprehensive guide explains what prompts are, outlines essential prompt components and multiple engineering frameworks, presents practical strategies for crafting clear and structured prompts, addresses model limitations such as hallucinations, and showcases a wide range of advanced prompting techniques with code examples.
The author tests Manus, a new general‑purpose AI agent that tops the GAIA benchmark, outperforms OpenAI’s DeepResearch, and automatically handles complex multi‑step tasks such as converting PDFs to PPTs and extracting invoice data, demonstrating a dramatic leap in autonomous AI capabilities.
The article evaluates the Manus agent’s current strengths in information‑gathering tasks, contrasts collaborative versus fully‑delegated agent models, identifies structural and context limitations that hinder professional‑grade outputs, and speculates on how future agents might bridge this gap.
Prompt engineering transforms vague queries into precise, reliable AI responses by structuring prompts with clear instructions, context, input, and output specifications, and by using role‑playing and formatting tricks, enabling models like DeepSeek and OpenAI to deliver accurate, consistent results across tasks.
The article analyzes DeepSeek‑R1’s low‑cost inference architecture, Chinese language optimizations, novel prompt‑engineering techniques, and the practical challenges of deploying large domestic models, offering insights into vertical AI applications and the evolving open‑source ecosystem in China.
Prompt Decorators are structured prefixes that standardize and enhance AI responses, addressing common challenges like vague prompts, inconsistent answers, and lack of reasoning by guiding the model to produce clear, logical, and well‑organized outputs across various use cases.
This guide shows how a full‑stack engineer can leverage Claude 3.7 Sonnet together with the Cursor AI editor to automatically create a full set of UX mock‑ups and HTML code for a Pilates fitness app, using prompt engineering, Ask mode, and step‑by‑step code aggregation.
The article details a step‑by‑step workflow that uses two carefully crafted prompts with Cursor to automatically locate source files in a large monorepo, record tasks, iteratively generate Vitest unit tests, track progress, and handle failures, turning a 11 k‑line codebase into a semi‑automated test suite.
While AI can generate code at unprecedented speed, this article argues that it does not eliminate software development complexity but merely shifts it to new layers such as prompt engineering, governance, and architectural consistency, leaving the core challenges intact.
This article examines DeepSeek, a Chinese AGI‑focused AI model, explains prompt‑engineering techniques, and evaluates its performance across database architecture, development, and operations tasks through concrete Q&A examples, SQL plan analysis, and shell‑script generation, while also discussing its broader impact on professionals, vendors and enterprises.
Hybrid reasoning lets Claude 3.7 Sonnet dynamically switch between fast, intuition‑like answers and step‑by‑step, deep analysis, improving both speed and accuracy for tasks ranging from simple code snippets to complex algorithm design, and signals a broader shift in large language model capabilities.
This article provides a step‑by‑step guide to creating a breakfast‑shop reception AI agent on Baidu's Wenxin Intelligent Agent platform, highlighting its core features, model selection with DeepSeek R1, and practical tips for configuring personas, knowledge bases, and plugins.
This article analyzes the evolution of AI‑assisted coding tools toward a 2.0 generation, outlines key trends such as agent‑driven architecture, developer‑first experience, and automated validation, and details the design and implementation of the AutoDev Sketch prototype that integrates high‑quality context, prompt engineering, and IDE‑native plugins.
This guide explains the fundamentals of prompt engineering for large language models such as DeepSeek and ChatGPT‑4o, illustrating clear‑prompt design, giving models time to think, chaining prompts, iterative refinement, and advanced tricks with concrete good and bad examples.
Discover a comprehensive collection of 24 structured prompting techniques—from basic role‑play formulas to advanced cross‑disciplinary and managerial frameworks—designed to help users of DeepSeek and other large language models craft precise, high‑impact queries that dramatically improve response quality and efficiency.
This guide presents five practical prompting techniques—including structured output, role‑playing, visual conversion, multi‑turn refinement, and multilingual handling—plus industry‑specific examples and common pitfalls, helping users craft precise commands for AI models like DeepSeek.
This article presents a systematic framework for crafting effective prompts, detailing the universal prompt template, role definition, task decomposition, RAG integration, few‑shot examples, memory handling, and parameter tuning to enhance large language model performance across diverse applications.
This article presents a comprehensive, step‑by‑step Prompt engineering framework—including role definition, problem description, goal setting, and requirement specification—augmented with techniques such as RAG, few‑shot examples, memory handling, and parameter tuning, enabling users to craft effective prompts for large language models across domains.
The article explains DeepSeek’s three core modes—V3, R1, and web‑search—details prompt‑engineering techniques for each mode, and demonstrates practical workplace applications such as data visualization, PPT generation, and new‑media copy creation, showing how tailored prompts can boost efficiency.
This guide presents seven practical prompt engineering tricks—ranging from precise requirement definition and contextual background provision to step‑by‑step decomposition, keyword tagging, iterative follow‑ups, tone/style adjustments, and model switching—that dramatically improve the relevance and quality of DeepSeek’s responses for work, learning, and creative tasks.
OpenAI has publicly disclosed the chain‑of‑thought reasoning of its o3‑mini model, prompting a wave of community experiments, critiques about authenticity, and discussions on the model’s limitations, prompting insights into AI interpretability and the trade‑offs of revealing internal reasoning.
This article explains what LLM agents are, their core components—brain, memory, planning, and tool use—illustrates how they handle complex queries through task decomposition, surveys notable frameworks, and discusses key challenges such as limited context, long‑term planning difficulties, output inconsistency, and prompt dependence.
This guide presents seven practical prompt‑engineering techniques—clear goals, structured queries, domain terminology, concrete examples, scoped questions, step‑by‑step breakdowns, and multi‑turn interactions—to help users get more accurate and useful answers from DeepSeek.
This article presents the concept of LLMOps—applying large language models to AIOps—by analyzing prompt challenges, introducing the LogPrompt engine for log analysis, describing a prompt‑learning data flywheel with CoachLM optimization, reporting experimental results, and outlining future multi‑modal directions.
This article details a step‑by‑step design of a RAG‑based intelligent Q&A assistant for the DeWu Open Platform, covering background, RAG fundamentals, system architecture, technology selection, prompt engineering with CO‑STAR, data preprocessing, vector store setup, LangChain.js implementation, similarity search, runnable chaining, debugging, and future prospects.
The MktAI Assistant combines LLM‑powered memory, skill planning, and tool‑calling with real‑time API data to replace slow, manual SQL dashboards, delivering sub‑minute, fresh, explainable marketing queries and attribution insights that boost decision speed, accuracy, and collaboration between data scientists and business users.
This article consolidates Google's Kaggle whitepaper on AI Agents, explaining their definition, key characteristics, core components—model, tools, and orchestration layer—along with architectural diagrams, learning techniques, and practical deployment steps on Vertex AI, offering a comprehensive guide for building generative AI agents.
This article outlines practical guidelines for effectively using AI-powered coding assistants, emphasizing task decomposition, precise requirement definition, leveraging context memory, and addressing common challenges such as quota limits, context loss, code disruption, and handling complex problems to maximize development efficiency.
This article proposes a quantitative model that breaks AI usage effectiveness into three multiplicative factors—professional ability, prompt engineering skills, and AI platform capabilities—detailing each component, offering a prompt framework (BROKE), and providing tailored recommendations for beginners, competitors, and applied learners.
This article provides a comprehensive overview of LLM-based Text-to-SQL technology, covering its background, evolution, challenges, various LLM-driven methods, benchmark datasets, evaluation metrics, and future research directions to guide researchers and practitioners in advancing natural language interfaces for databases.
The author experiments with GitHub Copilot Workspace to automatically generate a WeChat mini‑program for family library management, documents the prompting process, code generation, bug fixes, UI tweaks, and reflects on the broader impact of AI‑driven development on programmers' future jobs.
This article introduces several Chinese open‑source AI platforms, shows how to import their Java SDKs, obtain API keys, run test demos, encapsulate a reusable AI module with Spring Boot configuration, and apply prompt‑engineering techniques to generate AI‑driven questionnaire content.
This article explores the evolution, enterprise adoption, and practical usage of large‑model‑driven intelligent development tools, covering code‑completion advancements, full‑repo indexing, CI/CD integration, prompt engineering, inline chat interactions, and best practices for developers to collaborate effectively with AI.
This article presents a comprehensive analysis of Graph RAG, detailing its implementation workflow, step‑by‑step execution guide, four targeted optimization strategies, and experimental validation that demonstrates significant improvements in global and local question answering for industry scenarios.
This article walks through how the author leveraged GPT, Cursor, and carefully crafted prompts to automatically generate a fully functional TikTok‑style front‑end, from initial design screenshots to deployment on Vercel, all without manually writing any code.
The article explains how tokenization mismatches and biased next‑token prediction cause LLMs to miscount letters in “Strawberry” and incorrectly compare 9.9 versus 9.11, and shows that step‑by‑step Chain‑of‑Thought prompting with reason‑first output dramatically improves accuracy.
The article details Zhihu's technical salon on large‑model engineering, covering the RAG‑based Zhihu Direct Answer system, the open‑source ZhiLight inference framework, prompt engineering, agent research, and future plans for integrating AI into product and community workflows.
This article demonstrates how browser‑based AI web‑creation platforms like Bolt and v0 can generate complete front‑end code from natural‑language prompts or design images, optimize prompts, preview results, and publish a site with a single click, while also showing how to download the code for further development.
Tencent Cloud AI Code Assistant has evolved from token‑level IDE completions to LLM‑driven multi‑modal coding and chat features, employing a dual‑loop R&D system, Hunyuan‑based code models, and sophisticated trigger, prompt, stop, and display strategies to deliver context‑aware, secure, and efficient code generation within IDE and review environments.
The article explains how AI agents, integrated into transaction systems, automate log analysis, generate test data via natural-language tools, and preserve expert knowledge, achieving at least a 50 % boost in issue-tracing efficiency, reducing debugging time, and empowering developers to focus on feature development and stability.
This article introduces the “Thinking Claude” prompt that enhances Claude AI’s chain‑of‑thought reasoning, explains how to use it via the provided GitHub repository, and also showcases MD3‑Windows, an open‑source Rainmeter skin that brings Material You design to Windows desktops.
This article explains the concept, architecture, and step‑by‑step implementation of Retrieval Augmented Generation (RAG), covering indexing, retrieval & generation processes, a practical LangChain‑Redis‑llama.cpp example on Kubernetes, code snippets, test results, challenges, and references.
This guide explains when to rely on prompt engineering versus SFT fine‑tuning for Vision‑Language Models, emphasizing data quality, appropriate dataset sizes, training epochs, hyper‑parameter tuning, and practical steps to build robust VLM pipelines.
This article details how to design, implement, and evaluate a complex‑table intelligent Q&A solution for automotive maintenance using large language models, RAG pipelines, multi‑agent architectures, prompt engineering, and Alibaba Cloud services, achieving up to 93.8% accuracy.
This article explains what Prompt Engineering is, traces its development from early NLP commands to modern adaptive and multimodal prompting techniques, describes various prompting strategies such as Zero‑shot, Few‑shot, Chain‑of‑Thought, Auto‑CoT, and showcases a JD Logistics case study using these methods to classify product types with code examples.
This article explains what Prompt Engineering is, traces its development from early command‑based interactions to modern adaptive and multimodal prompting, details various prompting techniques such as zero‑shot, few‑shot, Chain‑of‑Thought, hallucination‑reduction methods, and demonstrates their practical use in a JD Logistics SKU piece‑type classification case with code examples.
This article explains what Prompt Engineering is, traces its evolution from early NLP commands to modern adaptive and multimodal prompting, details core techniques such as Zero‑shot, Chain‑of‑Thought, Auto‑CoT, and reduction of hallucinations, and showcases a logistics case study using various prompting strategies.
This article provides a comprehensive analysis of large language model hallucinations, detailing their definitions, classifications, root causes, detection techniques, and a wide range of mitigation approaches—including RAG pipelines, decoding strategies, and model‑enhancement methods—to improve reliability and safety in real‑world AI applications.
By leveraging multimodal large models such as GPT‑4o and fine‑tuned Qwen‑VL, the study builds a prompt‑engineered and SFT‑enhanced voucher verification system that classifies product categories, detects diverse defects, and estimates problem counts, achieving up to 90 % accuracy and meeting real‑time business throughput requirements.
The article reflects on Alibaba Cloud's large‑model deployments, outlines four service scenarios, examines three fundamental questions about foundation models, and offers a prioritized roadmap—including prompt engineering, RAG, and organizational changes—to effectively bring LLMs to production.
This article explains how AI tools like GPT and Midjourney can streamline the entire webtoon creation process—from extracting core policy content to generating high‑quality comic panels—showing a complete workflow that reduces production time from weeks to days.
This article introduces Retrieval‑Augmented Generation (RAG) as a solution to the hallucination, freshness, and data‑privacy issues of large language models, details its modular architecture, explains the layered system design and hybrid retrieval pipeline, and shares the practical challenges and engineering tricks encountered when deploying RAG in enterprise office scenarios.
This article introduces the Six Thinking Hats framework, explains its benefits, describes AI agent concepts and platforms, and provides a step‑by‑step guide with prompt examples for creating a low‑cost, fully‑featured Six Thinking Hats assistant using generative AI tools.
This paper presents the implementation and practice of generative retrieval based on Yuan large model in Tencent Advertising, addressing three key challenges: user intent capture, model alignment in advertising domain, and high-performance platform design under ROI constraints.
The article evaluates OpenAI’s o1 series—o1‑preview, o1‑mini and the upcoming full model—by comparing their complex reasoning strength, slower inference speed, higher pricing, API restrictions, and prompting best practices, helping developers decide when to adopt them.
This article details Ximalaya’s ChatBI project, describing how large language models are integrated into a BI platform to improve data accessibility, reduce development effort, and enhance query accuracy through prompt engineering, RAG, fine‑tuning, and multi‑agent architectures.
This article presents a systematic, four‑part Prompt engineering framework—role definition, problem description, goal setting, and requirement specification—augmented with RAG, few‑shot examples, memory handling, and model‑parameter tuning, enabling developers to craft high‑quality prompts for large language models across diverse tasks.
Using an LLM-powered IDE, the author built a full‑stack weekend project without writing a single line of code, discovering faster development cycles, new debugging habits, and the strengths and limits of AI assistants compared to traditional Google searches.
The article presents a universal four‑part prompt template—role, problem description, goal, and requirements—augmented with role definitions, RAG‑based knowledge retrieval, few‑shot examples, memory handling, temperature/top‑p tuning, and automated optimization techniques such as APE, APO, and OPRO, enabling developers to reliably craft high‑quality prompts for LLMs.
This article details Huolala's exploration of large‑language‑model (LLM) based AI assistants across multiple business scenarios, describing their architecture, implementation challenges, prompt engineering techniques, and the progressive stages from professional assistants to multi‑agent systems that drive efficiency and innovation in logistics.
The article explores how prompts have evolved from simple text cues into executable, shareable agents, outlining engineering best practices, DSL‑plus‑runtime architecture, and the Shire Run platform that enables downloading, sharing, and future online execution of AI‑driven smart agents.
This article explains Prompt Engineering, introduces the AI Gateway concept, and demonstrates how to integrate a Geo‑IP plugin with an AI prompt‑modifying plugin in Higress using Go and Wasm, providing configuration examples, implementation details, and sample request‑response scenarios.
LangChain is a modular framework that streamlines integration of large language models by providing unified model interfaces, prompt optimization, memory handling, indexing, chains, and agents, enabling developers to quickly build and deploy sophisticated NLP applications such as text generation, information extraction, and dynamic tool‑driven workflows across various industries.
This article provides a comprehensive overview of large language models, covering their fundamental concepts, historical milestones, parameter scaling, prompt engineering techniques, retrieval‑augmented generation, autonomous agents, and multimodal model applications, illustrating how these technologies reshape AI capabilities across domains.
Matt Welsh argues that despite fifty years of programming advances, humans remain poor at coding, and in the post‑AI era large language models will reshape software engineering, prompting a shift toward prompt engineering, new team roles, and a revival of fundamental engineering practices.
This article explores how AI-powered large language models enable Chinese commercial banks to automate, accurately match, and predict regulatory requirements, detailing new use‑cases, a prompt‑engineering framework, and the resulting efficiency and risk‑reduction benefits for the financial sector.
This comprehensive guide examines cutting‑edge prompt‑engineering strategies—covering clear instruction design, role‑playing, separators, step‑by‑step workflows, external tools, systematic testing, and case studies from Anthropic, Google, and practical Img2Code applications—to help developers achieve more accurate and powerful interactions with large language models.
The article details how Taobao’s AI team automated repetitive UI tasks for B2C front‑end development, achieving a 15 % efficiency gain across five projects, and outlines key challenges—prompt cost, low OCR accuracy, hallucinations, excess nodes, and customization variance—along with practical solutions such as a dedicated evaluation platform, OCR translation, model upgrades, prompt segmentation, output simplification, and a reusable component library.
